mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0004.nii'));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel/sub-0013/con_0004.nii'
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 24759576 bytes
Loading image number: 62
Elapsed time is 17.737822 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5981941 Bit rate: 22.51 bits
contrast_name = {'P_VC_cue_high_gt_low', 'V_PC_cue_high_gt_low', 'C_PV_cue_high_gt_low', ...
'P_VC_stimlin_high_gt_low', 'V_PC_stimlin_high_gt_low', 'C_PV_stimlin_high_gt_low',...
'P_VC_stimquad_med_gt_other', 'V_PC_stimquad_med_gt_other', 'C_PV_stimquad_med_gt_other',...
'P_VC_cue_int_stimlin','V_PC_cue_int_stimlin', 'C_PV_cue_int_stimlin',...
'P_VC_cue_int_stimquad','V_PC_cue_int_stimquad','C_PV_cue_int_stimquad',...
'P_simple_cue_high_gt_low', 'V_simple_cue_high_gt_low', 'C_simple_cue_high_gt_low', ...
'P_simple_stimlin_high_gt_low', 'V_simple_stimlin_high_gt_low', 'C_simple_stimlin_high_gt_low',...
'P_simple_stimquad_med_gt_other', 'V_simple_stimquad_med_gt_other', 'C_simple_stimquad_med_gt_other',...
'P_simple_cue_int_stimlin', 'V_simple_cue_int_stimlin', 'C_simple_cue_int_stimlin',...
'P_simple_cue_int_stimquad','V_simple_cue_int_stimquad','C_simple_cue_int_stimquad'
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
%disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 2 participants, size is now 60
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0093" "participants that are outliers:... sub-0098"
disp(n);
{'sub-0093'} {'sub-0098'}
t = ttest(imgs2);
One-sample t-test
Calculating t-statistics and p-values
orthviews(t);
SPM12: spm_check_registration (v7759) 12:36:49 - 20/01/2023
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.015496
Image 1
72 contig. clusters, sizes 1 to 29005
Positive effect: 30527 voxels, min p-value: 0.00000000
Negative effect: 414 voxels, min p-value: 0.00000906
orthviews(fdr_t);
SPM12: spm_check_registration (v7759) 12:36:51 - 20/01/2023
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
fdr_t = threshold(t, .001, 'fdr');
Image 1 FDR q < 0.001 threshold is 0.000052
Image 1
65 contig. clusters, sizes 1 to 2320
Positive effect: 5228 voxels, min p-value: 0.00000000
Negative effect: 3 voxels, min p-value: 0.00000906
orthviews(fdr_t);
SPM12: spm_check_registration (v7759) 12:36:52 - 20/01/2023
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects
sagittal montage: 51 voxels displayed, 5180 not displayed on these slices
sagittal montage: 130 voxels displayed, 5101 not displayed on these slices
sagittal montage: 84 voxels displayed, 5147 not displayed on these slices
axial montage: 758 voxels displayed, 4473 not displayed on these slices
axial montage: 741 voxels displayed, 4490 not displayed on these slices
[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1
fullpath_was_empty
_____________________________________________________________________
testr_low words_low testr_high words_high
_________ ______________ __________ ________________
-0.1575 {'depression'} 0.1763 {'movements' }
-0.14255 {'affect' } 0.17293 {'rest' }
-0.14088 {'reward' } 0.16409 {'lip' }
-0.13448 {'disorder' } 0.16254 {'hand' }
-0.13312 {'neutral' } 0.15759 {'sensorimotor'}
-0.12881 {'outcome' } 0.15566 {'action' }
-0.12665 {'anxiety' } 0.15446 {'actions' }
-0.12463 {'affective' } 0.13797 {'motor' }
-0.12107 {'conflict' } 0.13771 {'gestures' }
-0.11963 {'dopamine' } 0.13674 {'execution' }
% [image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( m, 'images_are_replicates', false, 'noverbose');
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0117 -1.8832 0.0644 0.0000
Cog Wholebrain 0.0118 2.3818 0.0204 1.0000
Emo Wholebrain 0.0005 0.0888 0.9295 0.0000
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ ________ ________ ________
{'Pain Wholebrain'} -0.01166 0.00619 -1.8837 0.064369 -0.23924
{'Cog Wholebrain' } 0.011769 0.0049426 2.3811 0.020397 0.3024
{'Emo Wholebrain' } 0.00051538 0.0058777 0.087685 0.93041 0.011136
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {62×3 cell}
text_han: {62×3 cell}
point_han: {62×3 cell}
star_handles: [9.0001 10.0001 11.0001]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ ___________ _________ ________ ________ _________
{'Pain Wholebrain'} -0.010009 0.0062817 -1.5933 0.11626 -0.20235
{'Cog Wholebrain' } 0.01146 0.0046536 2.4625 0.016635 0.31274
{'Emo Wholebrain' } -0.00095389 0.0058642 -0.16266 0.87132 -0.020658
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {62×3 cell}
text_han: {62×3 cell}
point_han: {62×3 cell}
star_handles: [12.0001 13.0001 14.0001]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0005.nii'));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel/sub-0013/con_0005.nii'
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 24759576 bytes
Loading image number: 62
Elapsed time is 30.059674 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5980912 Bit rate: 22.51 bits
contrast_name = {'P_VC_cue_high_gt_low', 'V_PC_cue_high_gt_low', 'C_PV_cue_high_gt_low', ...
'P_VC_stimlin_high_gt_low', 'V_PC_stimlin_high_gt_low', 'C_PV_stimlin_high_gt_low',...
'P_VC_stimquad_med_gt_other', 'V_PC_stimquad_med_gt_other', 'C_PV_stimquad_med_gt_other',...
'P_VC_cue_int_stimlin','V_PC_cue_int_stimlin', 'C_PV_cue_int_stimlin',...
'P_VC_cue_int_stimquad','V_PC_cue_int_stimquad','C_PV_cue_int_stimquad',...
'P_simple_cue_high_gt_low', 'V_simple_cue_high_gt_low', 'C_simple_cue_high_gt_low', ...
'P_simple_stimlin_high_gt_low', 'V_simple_stimlin_high_gt_low', 'C_simple_stimlin_high_gt_low',...
'P_simple_stimquad_med_gt_other', 'V_simple_stimquad_med_gt_other', 'C_simple_stimquad_med_gt_other',...
'P_simple_cue_int_stimlin', 'V_simple_cue_int_stimlin', 'C_simple_cue_int_stimlin',...
'P_simple_cue_int_stimquad','V_simple_cue_int_stimquad','C_simple_cue_int_stimquad'
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 4 participants, size is now 58
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are …" "participants that are …" "participants that are …" "participants that are …"
disp(n);
{'sub-0086'} {'sub-0093'} {'sub-0098'} {'sub-0112'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain 0.0039 0.6162 0.5401 0.0000
Cog Wholebrain 0.0012 0.2600 0.7957 0.0000
Emo Wholebrain -0.0047 -0.6838 0.4967 0.0000
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ ________ _______ ________
{'Pain Wholebrain'} 0.0038714 0.0062899 0.61549 0.54052 0.078168
{'Cog Wholebrain' } 0.0011533 0.0044483 0.25926 0.79631 0.032926
{'Emo Wholebrain' } -0.0046942 0.0068794 -0.68236 0.49759 -0.08666
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {62×3 cell}
text_han: {62×3 cell}
point_han: {62×3 cell}
star_handles: [9.0002 10.0002 11.0002]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ ________ _______ _________
{'Pain Wholebrain'} 0.0030414 0.0062263 0.48848 0.62696 0.062037
{'Cog Wholebrain' } 0.0010565 0.0041062 0.25729 0.79782 0.032675
{'Emo Wholebrain' } -0.0038861 0.00678 -0.57317 0.56863 -0.072793
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {62×3 cell}
text_han: {62×3 cell}
point_han: {62×3 cell}
star_handles: [12.0002 13.0002 14.0002]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0006.nii'));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel/sub-0013/con_0006.nii'
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 24759576 bytes
Loading image number: 62
Elapsed time is 12.557262 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5976073 Bit rate: 22.51 bits
contrast_name = {'P_VC_cue_high_gt_low', 'V_PC_cue_high_gt_low', 'C_PV_cue_high_gt_low', ...
'P_VC_stimlin_high_gt_low', 'V_PC_stimlin_high_gt_low', 'C_PV_stimlin_high_gt_low',...
'P_VC_stimquad_med_gt_other', 'V_PC_stimquad_med_gt_other', 'C_PV_stimquad_med_gt_other',...
'P_VC_cue_int_stimlin','V_PC_cue_int_stimlin', 'C_PV_cue_int_stimlin',...
'P_VC_cue_int_stimquad','V_PC_cue_int_stimquad','C_PV_cue_int_stimquad',...
'P_simple_cue_high_gt_low', 'V_simple_cue_high_gt_low', 'C_simple_cue_high_gt_low', ...
'P_simple_stimlin_high_gt_low', 'V_simple_stimlin_high_gt_low', 'C_simple_stimlin_high_gt_low',...
'P_simple_stimquad_med_gt_other', 'V_simple_stimquad_med_gt_other', 'C_simple_stimquad_med_gt_other',...
'P_simple_cue_int_stimlin', 'V_simple_cue_int_stimlin', 'C_simple_cue_int_stimlin',...
'P_simple_cue_int_stimquad','V_simple_cue_int_stimquad','C_simple_cue_int_stimquad'
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 3 participants, size is now 59
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:..…" "participants that are outliers:..…" "participants that are outliers:..…"
disp(n);
{'sub-0093'} {'sub-0098'} {'sub-0112'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain 0.0084 1.2632 0.2113 0.0000
Cog Wholebrain -0.0122 -2.5739 0.0125 1.0000
Emo Wholebrain 0.0030 0.4243 0.6728 0.0000
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ ________ ________
{'Pain Wholebrain'} 0.0084087 0.0066607 1.2624 0.2116 0.16033
{'Cog Wholebrain' } -0.012236 0.0047541 -2.5739 0.012502 -0.32688
{'Emo Wholebrain' } 0.0029794 0.0069939 0.426 0.67161 0.054102
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {62×3 cell}
text_han: {62×3 cell}
point_han: {62×3 cell}
star_handles: [9.0004 10.0004 11.0004]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ _________ ________
{'Pain Wholebrain'} 0.0072168 0.006324 1.1412 0.25826 0.14493
{'Cog Wholebrain' } -0.012135 0.004465 -2.7178 0.0085426 -0.34516
{'Emo Wholebrain' } 0.0042502 0.0067222 0.63226 0.52958 0.080297
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {62×3 cell}
text_han: {62×3 cell}
point_han: {62×3 cell}
star_handles: [12.0004 13.0004 14.0004]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
% pubfilename = '6cond_cueeffect_contrast.mlx';
% p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...
% 'format', 'html', 'outputDir', pubdir, ...
% 'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));
% htmlfile = publish(pubfilename, p);